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Contract No. DE-AC36-08GO28308

Connectivity-Enhanced Route 

Selection and Adaptive Control 

for the Chevrolet Volt

Preprint

Jeffrey Gonder and Eric Wood

National Renewable Energy Laboratory

Sai Rajagopalan

General Motors R&D

To be presented at the 21st World Congress on Intelligent 

Transport Systems

Detroit, Michigan

September 7–11, 2014

Conference Paper

NREL/CP-5400-60960

July 2014
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CONNECTIVITY-ENHANCED ROUTE SELECTION AND ADAPTIVE CONTROL 

FOR THE CHEVROLET VOLT

Jeffrey Gonder

National Renewable Energy Laboratory 

15013 Denver West Parkway 

Golden, CO 80401-3305 

303-275-4462 

Jeff.Gonder@nrel.gov

Eric Wood 

National Renewable Energy Laboratory 

15013 Denver West Parkway 

Golden, CO 80401-3305 

303-275-3290 

Eric.Wood@nrel.gov

Sai Rajagopalan 

General Motors R&D

30500 Mound Road 

Warren, MI 48090 

248-891-1737 

Sai.Rajagopalan@GM.com

ABSTRACT

The National Renewable Energy Laboratory and General Motors evaluated connectivity-enabled 

efficiency enhancements for the Chevrolet Volt. A high-level model was developed to predict 

vehicle fuel and electricity consumption based on driving characteristics and vehicle state inputs. 

These techniques were leveraged to optimize energy efficiency via green routing and intelligent 

control mode scheduling, which were evaluated using prospective driving routes between tens of 

thousands of real-world origin/destination pairs. The overall energy savings potential of green 

routing and intelligent mode scheduling was estimated at 5% and 3%, respectively. These 

represent substantial opportunities considering that they only require software adjustments to 

implement.

KEY WORDS

Intelligent Transportation Systems, Green Routing, Vehicle Drive Cycles/Profiles, RouteBased/Adaptive Powertrain Control, Vehicle Telematics/Navigation, Vehicle Energy Use 

Prediction

INTRODUCTION

Energy security, fuel cost, and air quality concerns have led to increased powertrain 

electrification in new vehicles. At the same time, the ubiquitous availability of advanced vehicle 

telematics systems, such as OnStar, has made real-time information on driving routes, traffic, 
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and road topology readily accessible. Together, these trends offer the potential for increased 

powertrain efficiency, particularly in vehicles with both a traction battery and a combustion 

engine. Such vehicles can leverage route-specific information to anticipate road loads and 

schedule power flows in the most efficient manner possible.

Significant research exists in the literature exploring pathways for increasingly connected 

vehicles to optimize modern transportation systems. Some example applications include: 

• Routing algorithms that leverage digital maps to make drive cycle predictions with the 

goals of minimizing travel time, energy use, and/or emissions [1-4] 

• Vehicle speed advisory programs targeted at encouraging efficient driving habits such 

as gradual accelerations and intermediate travel speeds [5-8] 

• Predictive powertrain controls that employ drive cycle predictions for optimal energy 

management in hybrid electric and plug-in hybrid electric vehicles [9-18] 

As a contribution to this growing field of research, the National Renewable Energy Laboratory 

(NREL) and General Motors, in collaboration with the U.S. Department of Energy, evaluated 

connectivity-enhanced route selection and adaptive control techniques to even further increase 

energy efficiency in the Chevrolet Volt platform. The project included both simulation and 

chassis dynamometer testing to develop energy prediction algorithms applied to the Volt over 

multiple real-world driving profiles. The algorithms were used to implement and evaluate green 

routing and adaptive intelligent control mode scheduling for the Volt over predicted travel 

routes. 

APPROACH

Figure 1 illustrates the overall methodology for developing the energy predictions that serve as 

the basis for the green routing and adaptive control energy efficiency enhancements. The process 

begins by identifying candidate routes for traveling between a given (or predicted) origin and 

destination (O/D) pair. Each route is divided into segments characterized by road type 

information and that may also take real-time traffic and driver aggression predictions as inputs. 

A drive cycle model (developed as part of this project) then makes predictions of drive cycle 

characteristics expected over each driving segment. Based on these cycle metrics, as well as road 

grade and the current vehicle/battery state, a look-up table model (also developed for this project 

specific to the Volt powertrain) estimates the vehicle’s segment-by-segment fuel and electricity 

consumption. 

This methodology, and particularly the drive cycle and Volt powertrain (PT) models illustrated 

in Figure 1, was very computationally heavy to develop, involving processing, analyzing and 

simulating hundreds of thousands of drive cycles. However, the resultant look-up table models 

become quite computationally light to implement in a vehicle by eliminating the need for 

predicting a second-by-second speed trace or for real-time simulation using a computationally 

intensive vehicle model. 
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Figure 1: Overall energy use estimation methodology—providing the basis for green routing and 

adaptive control efficiency enhancements.

RESULTS

Cycle Metric Prediction

After establishing one or more potential driving routes between a given O/D pair (including map 

matching each segment of the route to an underlying road layer from a provider such as 

NAVTEQ/Nokia/HERE), the project team drew on information such as road segment type 

(functional class [FC], speed category, etc.) from the underlying road layer to predict 

representative cycle metrics (such as average speed, acceleration, road grade, and stops per mile) 

over each segment of the driving route. A data-driven correlation between road type and drive 

cycle characteristics was established by analyzing thousands of second-by-second real-world 

driving profiles collected with global positioning system devices and archived in NREL’s 

Transportation Secure Data Center (TSDC) (19). After map-matching the TSDC driving profiles 

to the underlying road layer (as described above), the driving profiles were subdivided into 

smaller increments, such as the 0.1-mile “nanotrips” illustrated in Figure 2. 

Figure 2: Illustrative division of real-world driving data into smaller “nanotrip” distance 

intervals.
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The speed and acceleration characteristics for these nanotrips were then correlated to the road 

functional class being traversed (FC = 1 corresponds to high-throughput interstate travel, and FC 

= 5 corresponds to low-throughput neighborhood streets). As illustrated in Figure 3, this resulted 

in reasonable predictions of average speed and acceleration characteristics simply given 

information on the functional class of the current and the previous 0.1-mile segment of the given 

driving route. Further precision would be obtained by factoring in additional inputs, such as realtime traffic speeds over the given driving segment. 

Figure 3: Significant concentration of nanotrip data around hard accelerations when transitioning 

from an FC4 to an FC3 road segment (i.e., to a higher speed/capacity roadway). 

Energy Use Prediction

In the next component of the project, estimated cycle metrics (such as average speed, 

acceleration, road grade, etc.) were converted into vehicle energy use predictions over a given 

route. The method developed to accomplish this involved generating detailed energy use maps 

for the vehicle using detailed simulations (complemented by physical vehicle data collection) 

over tens of thousands of drive cycles. As mentioned in the Approach section, these energy use 

maps are computationally heavy to develop, but once they are built they are computationally 

light to implement for a green routing or dynamic control application.

For the Chevrolet Volt powertrain used in this study, the energy use maps included both 

electricity and fuel consumption relationships, and considered charge depleting (CD) operation, 

charge sustaining (CS) operation, and the need to track vehicle state-of-charge (SOC) via the 

electricity consumption relationships to determine the correct operating mode. The effort relied 

primarily upon simulations using an internal General Motors powertrain model and secondarily 

on test data collected from a Chevrolet Volt that had been modified to allow on-the-fly initiation 

of CS operation even at a high vehicle battery SOC. Note that the results shown here omit 

proprietary data values specific to the Volt powertrain, but nonetheless convey the relative trends 

and overall steps employed in the analysis. 

The simulation and test results were post-processed into nanotrips using the methods described 

in Figure 1. In addition to the previously mentioned cycle characteristic categorization (average 

speed, acceleration, etc.), the simulation and testing permitted associating each nanotrip with 
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values for electricity and fuel consumption (each value referenced as well to the battery SOC at 

the start of the nanotrip). Figure 4 illustrates a discretized look-up map of engine-off electric rate 

in the average speed and acceleration space. This map was derived from all the engine-off 

nanotrip simulation results, which showed consistent electric consumption rates for nanotrips 

with similar cycle characteristics.

Figure 4: Discretized correlation between cycle characteristics and electric consumption rate

generated from detailed simulation results when the Volt engine was off.

Figure 5 provides a similar visualization of the electric consumption rate while the vehicle 

engine is on, this time organized in a space defined by the SOC of the nanotrip (where the CS 

hold mode was engaged at a target SOC around the middle of the figure) and the product of the 

average speed and acceleration characteristics of the nanotrip. Consistent results within this 

space for nanotrips with similar characteristics again enable discretization of the data into a 

lookup map as illustrated by Figure 5. The engine-on fuel rate relationship was found to correlate 

well with the difference between the engine-on and engine-off electric rate estimates (as 

provided by the cycle-characteristic-based lookup maps in Figures 4 and 5). Figure 6 illustrates 

the resulting correlation established as compared to the actual results from the detailed 

simulation.

Figure 5: Discretized correlation between cycle characteristics, SOC, and electric consumption 

rate generated from detailed simulation results when the Volt engine was on. 
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Figure 6: Engine-on fuel rate estimates correlate with the difference or “error” between the 

hybrid (engine-on) electric rate and the all-electric (engine-off) electric rate estimates.

The described look-up maps/correlations define everything needed to predict energy use (fuel 

and electricity consumption) from speed and acceleration cycle characteristics. The predictions 

can be further refined by establishing similar correlations for additional cycle segment 

characteristics such as road grade. Figure 7 illustrates the translation model (trained by the 

detailed simulation and test data over cycles run at different grades) to estimate electric rate 

impact as a function of road grade for similarly characterized nanotrips. As shown in Figure 8, 

the grade-based translation model shows a very good ability to predict the grade-adjusted electric 

consumption rates based only on the zero-grade electric rate as an input.

As a point of validation, the energy prediction maps (trained on simulation data) were evaluated 

against test data from an instrumented Chevrolet Volt run on a vehicle dynamometer over a 

comprehensive range of driving conditions including combinations of speed/acceleration 

characteristics, positive and negative road grades, control modes (CD/CS), and battery SOCs. 

Figure 9 provides a snapshot of these comparisons by showing distributions of model error 

(energy prediction map less measured test data) for both electric and fuel use rates. Model error 

distributions can be seen to center around zero (represented by the red dashed lines). 

Figure 7: Grade-based electric rate translation model created to adjust the zero-grade lookup 

maps described in Figures 4–6 to account for driving segment grade. 
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Figure 8: The grade translation model agrees well with individual electric rate adjustments 

observed from simulation and test data over similarly characterized nanotrips at different road 

grades.

Figure 9: Distributions of modeled electric and fuel rate errors when comparing energy 

estimation maps to dynamometer test data (red dashed line represented zero error).

Green Routing

Evaluation of the connectivity-enabled green routing and route-based control enhancements 

again involved leveraging the TSDC—specifically the large set of real O/D locations contained 

in the database. NREL leveraged Google Maps’ application programming interface to generate 

route options between each O/D pair and applied the cycle metric and fuel/electricity prediction 

approach outlined above to evaluate each route. Figure 10 categorizes the results for the nearly 

43,000 O/D pairs and highlights that for many O/D pairs Google’s routing software either 

recommends only one route, or the fastest recommended route also turns out to be the most 

energy efficient route. However, 37% of the time the fastest route does not correspond with the 

greenest route, so that fraction of driving trips is taken to be the potential opportunity where 

green routing could result in energy-saving benefits (relative to the fastest route being the 

assumed default).
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Figure 10: 37% of the nearly 43,000 O/D pairs show green routing energy savings potential 

relative to the fastest route option. 

Under this set of assumptions, any green routing energy savings will come at a cost of increased 

travel time. Figure 11 explores this tradeoff by arranging the results as a function of the vehicle 

operator’s hypothetical monetary value of time spent in the vehicle. The top line in the plot 

corresponds to percent increased travel time, and the bottom line corresponds to percent energy 

and cost reductions provided by the “greenest” route. 

To facilitate interpretation, consider two example vertical slices on this plot. The points 

intersected by the y-axis represent the extreme scenario with no value of time penalty counted 

against increased time requirements by the green route. The aggregate results represented by this 

scenario could realize a 12.3% reduction in energy use and cost, but a 14.4% increase in travel 

time. As a second example, consider the vertical slice at a time value of $35/hr. For this scenario, 

energy use and cost could be decreased by 1.0% with a negligible increase in travel time. 

Figure 11: Trade-off between energy/cost savings and travel time increases as a function of 

passenger/driver value of time (for the 37% of O/D pairs where the least energy-consuming route 

prediction was not the fastest route).
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Control Mode Scheduling

The intelligent CD vs. CS mode scheduling evaluation involved similar large-scale analysis, 

specifically, of over 100,000 potential routes identified from the TSDC O/D database. The 

evaluation required first adding an extra analysis layer to compare the default mode schedule 

(CD followed by CS) as compared to the optimal (least fuel consuming) mode schedule. As a 

simplified overview, the methodology included in the added layer begins by assuming that all 

driving could be accomplished in CD mode (initially ignoring energy limits of the vehicle 

battery). It then incrementally substitutes driving segments from CD to CS operation, until the 

final trip SOC equals that from the default CD followed by CS operation. Trip segments are 

prioritized for substituting from CD to CS control based on minimizing the cost/benefit ratio of 

doing so, where the cost is defined as the increase fuel use incurred by the substitution and the 

benefit is defined as the decreased electric depletion rate.

The top plot in Figure 12 shows an example nominal vs. optimal battery SOC depletion profile 

generated from the methodology described above. Note that for evaluating this project’s highlevel CD vs. CS mode scheduling opportunity, each considered trip was assumed to begin at an 

initial SOC such that 50% or more of the trip was completed in CS mode. The bottom plot in 

Figure 11 shows the corresponding fuel consumption profile for the nominal vs. optimal control 

mode scheduling over the example profile. In this example, fuel consumption was reduced 25% 

by scheduling CS operation during highway driving segments in the first half of the route, and 

saving CD operation for the city driving anticipated at the end of the route. 

Figure 12: Nominal (solid blue line) vs. optimal (dashed red line) battery SOC and fuel use 

profiles for one example route. 

Figure 13 shows the scatter of fuel savings opportunity from optimal mode scheduling applied to 

over 100,000 trips. These results indicated that very large fuel savings results predominantly 

occur at short driving distances, which may be an artifact of the reduced starting SOC approach 

applied for these shorter trips. However, a number of trips (even longer than 30–40 miles in 

length) realize fuel savings on the order of 10%, and the average fuel savings across all trips 

exceeds 3%.
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Figure 13: Efficiency improvements from control mode scheduling optimization evaluated across 

over 100,000 representative driving routes. 

CONCLUSIONS

Under this project, NREL collaborated with General Motors to evaluate connectivity-enabled 

efficiency enhancements for the Chevrolet Volt. Project accomplishments included developing 

and demonstrating the ability to estimate drive cycle characteristics over anticipated driving 

routes. The project team further developed a high-level model to predict vehicle fuel and 

electricity consumption based on driving characteristic and vehicle state inputs. The team 

combined and leveraged these techniques in pursuit of energy efficiency optimization via green 

routing and intelligent control mode scheduling. 

The green routing and intelligent control mode scheduling enhancements were evaluated using 

prospective driving routes between tens of thousands of real-world O/D pairs. Considering the 

aggregate green routing benefit multiplied by the fraction of O/D pairs where the default (fastest) 

route consumed more fuel, the overall green routing fuel savings opportunity could approach 5% 

(assuming a low value of passenger time). The average efficiency benefit from intelligent highlevel scheduling of CS vs. CD control showed a similar magnitude—a little over 3% potential 

fuel savings on trips that require some mix of CS and CD operation. An 8% fuel savings (when 

taken as additive benefits) represents a substantial opportunity especially considering that only 

software adjustments are required to realize this efficiency gain. 

Future work efforts could include adding route-based optimization to lower-level controls (in 

addition to the high-level CD vs. CS control mode scheduling), formally incorporating real-time 

traffic/congestion information to further improve cycle metric predictions, and evaluating result 

sensitivity to various conditions and erroneous predictions. Additional options could include 

implementing, refining and more robustly testing both green routing and adaptive control 

approaches in a development vehicle, and/or considering additional vehicle 

platforms/powertrains.
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ACKNOWLEDGEMENTS

This study was jointly supported by the U.S. Department of Energy’s Vehicle Technologies 

Office and General Motors. The authors would specifically like to thank David Anderson of the 

U.S. Department of Energy and Chen Fang Chang of General Motors for their guidance and 

support. 

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